Publications by authors named "Diego Candia-Rivera"

Article Synopsis
  • * Standard methods divide HRV into frequency bands, which can sometimes lead to inaccurate measurements, especially in the low-frequency range.
  • * The researchers propose a new estimator that uses interbeat interval data from electrocardiograms (ECGs) to better analyze HR and HRV dynamics, showing improved performance compared to traditional methods and providing a useful tool for studying autonomic activity.
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The interplay between the brain and interoceptive signals is key in maintaining internal balance and orchestrating neural dynamics, encompassing influences on perceptual and self-awareness. Central to this interplay is the differentiation between the external world, others and the self, a cornerstone in the construction of bodily self-awareness. This review synthesizes physiological and behavioral evidence illustrating how interoceptive signals can mediate or influence bodily self-awareness, by encompassing interactions with various sensory modalities.

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Article Synopsis
  • Recent research highlights the importance of studying interactions between the brain and heart, with new methodologies revealing insights into neural functions.
  • Current frameworks often examine single brain regions in relation to heartbeat dynamics, missing the complexity of dynamic brain networks responding to various demands.
  • This study proposes a novel framework that analyzes the relationship between cortical networks and cardiac activity using EEG data, suggesting that understanding this interplay could lead to better diagnostic tools for emotional states and conditions like Parkinson's disease.*
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Article Synopsis
  • Hypoxic ischemic brain injury (HIBI) affects brain areas crucial for the autonomic nervous system, and researchers aimed to find how heart rate variability (HRV) can predict neurological outcomes after cardiac arrest (CA).
  • The study included 199 patients who experienced CA, focusing on the correlation between HRV markers and the severity of brain injury, along with other prognostic indicators like EEG results and pupillary reflexes.
  • Findings revealed that patients with better neurological outcomes had significantly higher HRV values, particularly in very low and low frequencies, as well as in the LF/HF ratio, indicating that these HRV measurements may serve as useful predictors for recovery.
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The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies.

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Article Synopsis
  • Parkinson's disease (PD) is linked to disrupted brain connectivity and autonomic dysfunction, leading to motor and cognitive decline, as well as reduced sensitivity to cardiac inputs.
  • This study investigates the relationship between brain-heart coupling and motor symptoms in PD by analyzing electroencephalogram (EEG) and electrocardiogram (ECG) data from patients, both on and off medication, and comparing it to healthy individuals.
  • Results indicate that PD patients show reduced brain-heart coupling, particularly noticeable in specific brain wave patterns, but this coupling improves with dopaminergic medication in correlation with reduced motor symptoms, suggesting a new avenue for understanding and evaluating early PD.
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Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods.

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Objective: To investigate autonomic nervous system activity measured by brain-heart interactions in comatose patients after cardiac arrest in relation to the severity and prognosis of hypoxic-ischemic brain injury.

Methods: Strength and complexity of bidirectional interactions between EEG frequency bands (delta, theta, and alpha) and ECG heart rate variability frequency bands (low frequency, LF and high frequency, HF) were computed using a synthetic data generation model. Primary outcome was the severity of brain injury, assessed by (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels.

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Article Synopsis
  • - Recent research indicates that interactions between brain and heart influence our perception and self-awareness, with visceral inputs potentially shaping subjective experiences.
  • - This study examines how auditory irregularities affect both the brain's direct response to sound (measured as ERPs) and responses to heartbeats (measured as HERs) in patients with disorders of consciousness.
  • - Results reveal that distinct ERPs and HERs are produced by local and global auditory effects, helping to differentiate between minimally conscious and unresponsive wakefulness patients; HERs may serve as a new, cost-effective tool for diagnosing consciousness.
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Functional Near Infrared Spectroscopy (fNIRS) is a useful tool for measuring hemoglobin concentration. Linear theory of the hemodynamic response function supports low frequency analysis (<0.2 Hz).

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Background And Purpose: Whether brain-heart communication continues under ventricular fibrillation (VF) remains to be determined. There is weak evidence of physiological changes in cortical activity under VF. Moreover, brain-heart communication has not previously been studied in this condition.

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Because consciousness does not necessarily translate into overt behaviour, detecting residual consciousness in noncommunicating patients remains a challenge. Bedside diagnostic methods based on EEG are promising and cost-effective alternatives to detect residual consciousness. Recent evidence showed that the cortical activations triggered by each heartbeat, namely, heartbeat-evoked responses (HERs), can detect through machine learning the presence of minimal consciousness and distinguish between overt and covert minimal consciousness.

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Recent studies suggest that the interaction between the brain and heart plays a key role in cognitive processes, and measuring these interactions is crucial for understanding the interaction between the central and autonomic nervous systems. However, studying this bidirectional interplay presents methodological challenges, and there is still much room for exploration. This paper presents a new computational method called the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG) for estimating brain-heart interactions.

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Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown.

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Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions.

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Recent studies have established that cardiac and respiratory phases can modulate perception and related neural dynamics. While heart rate and respiratory sinus arrhythmia possibly affect interoception biomarkers, such as heartbeat-evoked potentials, the relative changes in heart rate and cardiorespiratory dynamics in interoceptive processes have not yet been investigated. In this study, we investigated the variation in heart and breathing rates, as well as higher functional dynamics including cardiorespiratory correlation and frontal hemodynamics measured with fNIRS, during a heartbeat counting task.

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Functional near infrared spectroscopy (fNIRS) is a modality that can measure shallow cortical brain signals and also contains pulsatile oscillations that originate from heartbeat dynamics. In particular, while fNIRS slow waves (0 Hz to 0.6 Hz) refer to the standard hemodynamic signal, fast-wave (0.

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Article Synopsis
  • The study focuses on understanding the interactions between the brain and heart, specifically how the autonomic nervous system connects them, referred to as brain-heart interplay (BHI).
  • The researchers developed a new model called the Sympathovagal Synthetic Data Generation Model, which uses advanced techniques to analyze how cardiac activity influences brain activity in real-time, rather than relying on traditional methods.
  • In a preliminary experiment with 16 participants experiencing cold stress, the findings indicated that thermal stress activates a functional connection from the heart to the brain, affecting brainwave patterns, especially in specific frequency bands (δ and γ).
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A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body.

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Article Synopsis
  • - The study investigates the interplay between brain and heart functions, focusing on how these systems communicate and impact cognitive processes, highlighting gaps in current research.
  • - A new computational model, called the Sympatho-Vagal Synthetic Data Generation Model, was developed to analyze this interaction using EEG and cardiac data from 26 participants during a cold-pressor test.
  • - Results indicate that thermal stress leads to a significant heart-to-brain interaction driven by EEG activity, while brain-to-heart communication is influenced by central brain regions, demonstrating the importance of sympathetic control in these dynamics.
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Background: The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet.

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The neural monitoring of visceral inputs might play a role in first-person perspective (i.e., the unified viewpoint of subjective experience).

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Article Synopsis
  • * Four EEG referencing techniques were tested—Cz channel, common average, mastoids average, and Laplacian—while using various BHI methods like heartbeat-evoked potentials and maximal information coefficient (MIC).
  • * Results reveal that the choice of EEG reference significantly influences BHI measurements, with noteworthy differences noted particularly with the Laplacian reference and MIC analysis.
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